The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Deep code search
Abstract
To implement a program functionality, developers can reuse previously written code snippets by searching through a large-scale codebase. Over the years, many code search tools have been proposed to help developers. The existing approaches often treat source code as textual documents and utilize information retrieval models to retrieve relevant code snippets that match a given query. These approaches mainly rely on the textual similarity between source code and natural language query. They lack a deep understanding of the semantics of queries and source code.
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Cited by
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Large Language Models for Software Engineering: A Systematic Literature Review
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
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“Some of the early applications of machine learning of source code include code search [1, 2], bug detection and localization [3], program synthesis [4], code summarization [5] and code completion [6–10].”
BackgroundAn Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
Tufano, M., Watson, C., Bavota, G., Penta, M. D., White, M., & Poshyvanyk, D. (2019) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3340544 · Cited by 370
Metadata
- Authors
- Gu, X., Zhang, H., & Kim, S.
- Venue
- Proceedings of the 40th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2018-05-27
- Pages
- 933-944
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2794601162
- Cited by
- 547
- References
- 8
- FWCI
- 45.64
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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